Skip to content

{ Tag Archives } news

Google News adds Badges

Via Nick Diakopoulos, I see that Google News has added badges, awarded for reading an number of articles on a certain topics. You earn them privately and can then share them. Nick has some thoughts on his blog.

I agree with a lot of Nick’s thoughts. Having validated reading behavior is useful – though it’s also interesting to get the difference between what topics people read and what topics people want others to know they read. As Nick points out, it might be a way for people to communicate to others that they are an expert on a topic – or at least an informed reader, as I suspect that experts may have other channels for following the topics about which they care most.

Though BunchBall sort of looks down on the quantified self aspect, I do think it’s useful to give people feedback on what they are reading (sort of like for news topics rather than what they think they read, though badges probably aren’t quite as data-rich as I’d want. At Michigan, we’re trying a similar experiment as part of the BALANCE project shortly, to assess whether feedback on past reading behavior affects the balance of political articles that subjects read.

If people do care about earning the badges, either to learn about their reading behavior or to share with others as a sign of their expertise or interests, then they’ll probably read more of their news through Google news – so that it is tracked in more detail. Thus, a win for Google, who gets the pageviews and data.

Google, why do you want me to earn a Kindle badge?

Influence When I first visited, I was encouraged to earn a Kindle badge. I couldn’t figure this out. Yeah, it’s an interesting product, but I don’t want to read a lot of news about it and a review of my Google News history showed that I never had through the site. So why, of all the >500 badges that Google could suggest to me (many for topics I read lots about), is it suggesting Kindle and only Kindle? If left me wondering if it was a random recommendation, if whatever Google used to suggest a badge was not very good for me, or it was a sponsored badge intended to get me to read more about Kindles (speaking of potential wins for Google…).

Whatever the case, this highlights a way that badges could push reading behavior – assuming that people want to earn, or want to avoid earning, badges. This can run both ways. Maybe someone is motivated by gadget badges and so reads more about Kindles; maybe someone doesn’t think of themselves as interested in celebrities or media and is thus pushed to read fewer articles about those topics than they were before. I’m not saying this is bad, per se, as feedback is an important part of self-regulation, but if badges matter to people, the simple design choice of which badges to offer (and promote) will be influential, just as the selection and presentation of articles are.

Sidelines at ICWSM

Last week I presented our first Sidelines paper (with Daniel Zhou and Paul Resnick) at ICWSM in San Jose. Slides (hosted on slideshare) are embedded below, or you can watch a video of most of the talk on VideoLectures.

Opinion and topic diversity in the output sets can provide individual and societal benefits. If news aggregators relying on votes and links to select and subsets of the large quantity of news and opinion items generated each day simply select the most popular items may not yield as much diversity as is present in the overall pool of votes and links.

To help measure how well any given approach does at achieving these goals, we developed three diversity metrics that address different dimensions of diversity: inclusion/exclusion, nonalienation, and proportional representation (based on KL divergence).

To increase diversity in result sets chosen based on user votes (or things like votes), we developed the sidelines algorithm. This algorithm temporarily suppresses a voter’s preferences after a preferred item has been selected. In comparison to collections of the most popular items, from user votes on and links from a panel of political blogs, the Sidelines algorithm increased inclusion while decreasing alienation. For the blog links, a set with known political preferences, we also found that Sidelines improved proportional representation.

Our approach differs and is complementary to work that selects for diversity or identifies bias based on classifying content (e.g. Park et al, NewsCube; ) or by classifying referring blogs or voters (e.g. Gamon et al, BLEWS). While Sidelines requires votes (or something like votes), it doesn’t require any information about content, voters, or long term voting histories. This is particularly useful for emerging topics and opinion groups, as well as for non-textual items.

bias mining in political bloggers’ link patterns

I was pretty excited by the work that Andy Baio and Joshua Schachter did to identify and show the political leanings in the link behavior of blogs that are monitored by Memeorandum. They used singular value decomposition [1] on an adjacency matrix between sources and items based on link data from 360 snapshots of Memeorandum’s front page.

For the political news aggregator project, we’ve been gathering link data from about 500 blogs. Our list of sources is less than half of theirs (I only include blogs that make full posts available in their feeds), but we do have full link data rather than snapshots, so I was curious if we would get similar results.

The first 10 columns of two different U matrices are below. They are both based on link data from 3 October to 7 November; the first includes items that had an in-degree of at least 4 (5934 items), the second includes items with an in-degree of at least 3 (9722 items). In the first, the second column (v2) seems to correspond fairly well to the political leaning of the blog; in the second, the second column (v3) is better.

I’ll be the first to say that I haven’t had much time look at these results in any detail, and, as some of the commenters on Andy’s post noted, there are probably better approaches for identifying bias than SVD. If you’d like to play too, you can download a csv file with the sources and all links with an in-degree >= 2 (21517 items, 481 sources). Each row consists of the source title, source url, and then a list of the items the source linked to from 3 October to 7 November. Some sources were added part way though this window, and I didn’t collect link data from before they were added.

[1] One of the more helpful singular value decomposition tutorials I found was written by Kirk Baker and is available in PDF.

US political news and opinion aggregation

Working with Paul Resnick and Xiaodan Zhou, I’ve started a project to build political news aggregators that better reflect diversity and represent their users, even when there is an unknown political bias in the inputs. We’ll have more on this to say later, but for now we’re making available a Google gadget based on a prototype aggregator’s results.

The list of links is generated from link data from about 500 blogs and refreshed every 30 minutes. Some of the results will be news stories, some will be op-ed columns from major media services, others will be blog posts, and there are also some other assorted links.

At this early point in our work, the results tend to be more politically diverse than an aggregator such as Digg, but suffer from problems with redundancy (we aren’t clustering links about the same story yet). As our results get better, the set of links the gadget shows should improve.

Update 15 December: I twittered last week that I’ve added bias highlighting to the widget, but I should expand a bit on that here.

Inspired by Baio and Schachter’s coloring of political bias on Memeorandum, I’ve added a similar feature to the news aggregator widget. Links are colored according the average bias of the blogs linking to them. This is not always a good predictor of the item’s bias or whether it better supports a liberal or conservative view. Sometimes a conservative blogger writes a post to which more liberal bloggers than conservative bloggers, and in that case, the link will be colored blue.

If you don’t like the highlighting, you can turn it off in the settings.